supervised UMAP working with metadata

Class_Mod/UMAP_.py

 # UMAP function for the Sample Selection module
-from Packages import * 
-from Class_Mod.DATA_HANDLING import * 
+from Packages import *
+from Class_Mod.DATA_HANDLING import *
 
 
 class Umap:
@@ -10,13 +10,14 @@ class Umap:
     def __init__(self, data_import, numerical_data, cat_data):
         self.x = data_import
         self.numerical_data = numerical_data
-        if len(cat_data) > 0:
+        if cat_data is None:
+            self.categorical_data_encoded = cat_data
+        elif len(cat_data) > 0:
             self.categorical_data = cat_data
             self.le = LabelEncoder()
             self.categorical_data_encoded = self.le.fit_transform(self.categorical_data)
-
         else:
-            self.categorical_data = False
+            self.categorical_data_encoded = None
 
         self.model = UMAP(n_neighbors=20, n_components=3, min_dist=0.0, random_state=42,)
         self.model.fit(self.numerical_data, y = self.categorical_data_encoded)

pages/1-samples_selection.py

@@ -88,7 +88,13 @@ if not spectra.empty:
     if dim_red_method == dim_red_methods[1]:
         dr_model = LinearPCA(xc, Ncomp=5)
     elif dim_red_method == dim_red_methods[2]:
-        dr_model = Umap(data_import = imp, numerical_data = MinMaxScale(spectra), cat_data = meta_data)
+        if not meta_data.empty:
+            filter = meta_data.columns[1:]
+            col = pc.selectbox('Supervised UMAP by:', options= filter, key=108)
+            supervised = meta_data[col]
+        else:
+            supervised = None
+        dr_model = Umap(data_import = imp, numerical_data = MinMaxScale(spectra), cat_data = supervised)
 
     if dr_model:
         axis1 = pc.selectbox("x-axis", options = dr_model.scores_.columns, index=0)
@@ -109,7 +115,7 @@ if not t.empty:
         data, labels = cl_model.fit_optimal(nclusters = ncluster)
 
     elif clus_method == cluster_methods[2]:
-        optimized_hdbscan = Hdbscan(model.scores_raw_)
+        optimized_hdbscan = Hdbscan(dr_model.scores_raw_)
         labels, hdbscan_score = optimized_hdbscan.HDBSCAN_scores_
 
 ##### Plots